一種新的數(shù)據(jù)流模糊聚類方法
doi: 10.11999/JEIT141415
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2.
(南京郵電大學(xué)計算機學(xué)院 南京 210003) ②(南京郵電大學(xué)江蘇省無線傳感網(wǎng)高技術(shù)研究重點實驗室 南京 210003)
基金項目:
國家自然科學(xué)基金(61171053, 61300239)，教育部博士點基金(20113223110002)，中國博士后科學(xué)基金(2014M551635)和江蘇省博士后科研資助計劃項目(1302085B)資助課題
New Fuzzy-Clustering Algorithm for Data Stream
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2.
(College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)
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摘要: 針對數(shù)據(jù)流上的聚類任務(wù)受到時間、空間限制等問題，該文提出一種基于權(quán)值衰減的數(shù)據(jù)流模糊微簇聚類算法(WDSMC)。該算法使用改進的帶權(quán)值的模糊C均值算法進行處理，并采用微簇結(jié)構(gòu)和權(quán)值時間衰減結(jié)構(gòu)提高聚類質(zhì)量。實驗表明，相對于現(xiàn)有的數(shù)據(jù)流加權(quán)模糊C均值聚類(SWFCM)算法和StreamKM++算法而言，WDSMC算法具有更好的聚類精度。
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關(guān)鍵詞:
- 數(shù)據(jù)挖掘 /
- 數(shù)據(jù)流 /
- 模糊C均值聚類 /
- 權(quán)值衰減 /
- 微簇聚類
Abstract: There is a great challenge in the data stream clustering due to a limitation of time and space. In order to solve this problem, a new fuzzy-clustering algorithm, called Weight Decay Streaming Micro Clustering (WDSMC), is presented in this paper. The algorithm uses a reformed weighted Fuzzy C-Means (FCM) algorithm, and improves the quality of clustering by the structures of micro-clusters and weight-decay. Experimental results show that this algorithm has better accuracy than Stream Weight Fuzzy C-Means (SWFCM) and StreamKM++ algorithm.-
Key words:
- Data mining /
- Data stream /
- Fuzzy C-Means (FCM) /
- Weight decay /
- Micro-clustering
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